SEO + Content Case Study

Bank Achieves 100% Growth in Credit Card Applications While Cutting Ad Spend by 35%

By focusing on the user’s journey through the funnel, both leads and lead quality saw an increase.

  • 100.25%

    increase in applications

  • 35.9%

    decrease in cost per acquisition

The Client

The Client is a full-service military bank that provides financial services for active-duty and retired military members, veterans, and their families nationwide.

The Objective

To increase the quality and lower the cost per acquisition on secured credit card applications.

Paid Media Strategy

  •  

    The problem

  • The client wanted to increase the number of applications for secured credit cards, but the initial application volume and quality were too low, and the cost per acquisition was too high.

  •  

    The solution

  • To create a customer journey campaign through high, medium, and low intent Search campaigns; driving prospecting audiences to educational blog posts; and using remarketing on both Search and non-Search channels to re-engage users.

    For the low-intent and non-Search prospecting campaigns, we set up a soft conversion for the max conversions bidding strategy. Knowing that we were unlikely to drive completed applications from an educational blog post on the Display network, we set the campaign goal to “view application page.” This event required the user to take two additional steps from the landing page. Doing so provided Google’s automated bidding signals to show our ads to users most likely to get to that third step.

    During early keyword optimizations, we ascertained that there was a knowledge gap between users searching for a “secured credit card” versus a “credit building credit card.” In the first example, users are indicating through their search terms that they know what a secured credit card is and that it’s the right product for them. In the latter, they know there is an existing product that can help them build their credit, but they don’t have all the information yet.

    Therefore, we divided Search campaigns by keyword intent into high, medium, and low segments. High-intent keywords were action-oriented: “apply for a secured credit.” Medium intent keywords focused on “credit builder cards” and “credit building credit cards.” In both these campaigns, users were sent to the first step in the application process, and the campaign goal was set to optimize toward application completions. The low-intent segment targeted info-seeking keywords, like “how do i build credit.” These users were sent to one of two educational blog posts (A/B test results for the blog pages were inconclusive as they performed similarly), with page views set as the primary conversion.

    From here, we created remarketing campaigns on Display and Demand Gen. Segmenting audiences based on different points in the users’ journey allowed us to retarget users based on where they are in the marketing funnel and serve them customized messaging.

  •  

    Why it matters

  • When we initially launched a credit builder campaign, the Client found that the application quality was low and the cost per acquisition was higher than the average deposit used to fund a new account. We found that users did not understand what a secured credit card is or how it requires a linked checking account. We needed to think of a way to educate users in the credit-building process so they understood that this is a specific type of credit card that helps users establish or rebuild credit by requiring a security deposit.



Comparing July to June, the cost per acquisition (CPA) decreased 32.93% campaign-specific and 35.9% account-wide.



Applications from the dedicated secured credit card campaigns increased by 100.25% in July compared to June. Account-wide, applications increased by 45.27%.



By meeting the user where they are in their Search journey, we increased the volume and quality of applications and decreased the CPA.
In this study, the campaigns had at least 30 days to acquire data prior to the study’s time frame. This allowed time for Google’s machine learning to take effect.

Results

By providing prospective customers with educational materials and re-engaging them through remarketing tactics, we increased paid search and direct traffic (and possibly stolen from Organic), increased application volume by 45% account-wide, and decreased the cost per application by 35% account-wide.

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